release: spotoptim 1.0.1 (max_surrogate_points default 30)

README.md

@@ -130,7 +130,7 @@ result = optimizer.optimize()
 - `tolerance_x` (float, optional): Minimum distance between points
 - `seed` (int, optional): Random seed for reproducibility
 - `verbose` (bool, default=False): Print progress information
-- `max_surrogate_points` (int, optional): Maximum number of points for surrogate fitting (default: None, use all points)
+- `max_surrogate_points` (int, optional): Maximum number of points for surrogate fitting (default: 30; set to None to use all points)
 - `selection_method` (str, default='distant'): Point selection method ('distant' or 'best')
 
 **Methods:**

src/spotoptim/SpotOptim.py

@@ -58,7 +58,7 @@ class SpotOptimConfig:
         verbose (bool): Whether to print verbose output.
         warnings_filter (Literal["default", "error", "ignore"]): Filter for warnings.
         n_infill_points (int): Number of infill points.
-        max_surrogate_points (Optional[Union[int, List[int]]]): Maximum number of surrogate points.
+        max_surrogate_points (Optional[Union[int, List[int]]]): Maximum number of surrogate points. Defaults to 30.
         selection_method (str): Method for selecting infill points.
         acquisition_failure_strategy (str): Strategy for handling acquisition function failures.
         penalty (bool): Whether to use penalty.
@@ -116,7 +116,7 @@ class SpotOptimConfig:
              verbose=False,
              warnings_filter="ignore",
              n_infill_points=1,
-             max_surrogate_points=None,
+             max_surrogate_points=30,
              selection_method="distant",
              acquisition_failure_strategy="random",
              penalty=False,
@@ -161,7 +161,7 @@ class SpotOptimConfig:
     verbose: bool = False
     warnings_filter: Literal["default", "error", "ignore"] = "ignore"
     n_infill_points: int = 1
-    max_surrogate_points: Optional[Union[int, List[int]]] = None
+    max_surrogate_points: Optional[Union[int, List[int]]] = 30
     selection_method: str = "distant"
     acquisition_failure_strategy: str = "random"
     penalty: bool = False
@@ -351,7 +351,7 @@ class SpotOptim(BaseEstimator):
         max_surrogate_points (int, optional):
             Maximum number of points to use for surrogate model fitting.
             If None, all points are used. If the number of evaluated points exceeds this limit,
-            a subset is selected using the selection method. Defaults to None.
+            a subset is selected using the selection method. Defaults to 30.
         selection_method (str, optional):
             Method for selecting points when max_surrogate_points is exceeded.
             Options: 'distant' (Select points that are distant from each other via K-means clustering) or
@@ -733,7 +733,7 @@ def __init__(
         verbose: bool = False,
         warnings_filter: Literal["default", "error", "ignore"] = "ignore",
         n_infill_points: int = 1,
-        max_surrogate_points: Optional[Union[int, List[int]]] = None,
+        max_surrogate_points: Optional[Union[int, List[int]]] = 30,
         selection_method: str = "distant",
         acquisition_failure_strategy: str = "random",
         penalty: bool = False,

tests/test_tricands.py

@@ -17,6 +17,13 @@ class TestTricands:
     """Test suite for tricands module."""
 
     def setup_method(self):
+        # Close figures leaked by earlier tests on the same xdist worker:
+        # test_visualization_2d mocks plt.figure, so tricands draws into
+        # whatever Axes gca() finds — a leaked 3D Axes makes scatter fail
+        # with "s must be a scalar, or float array-like ...".
+        import matplotlib.pyplot as plt
+
+        plt.close("all")
         # Create invalid (too few points) X
         self.X_small = np.array([[0.1, 0.1]])
         # Create valid 2D X